Search NASA⌕ Search

SEARCH · Search NASA

Results for “State Estimation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

State Estimation for K9

The goal of 'Estimate' is to take advantage of attitude information to produce better pose while staying flexible and robust. Currently there are several instruments that are used for attitude: gyros, inclinometers, and compasses. However, precise and useful attitude information cannot come from one instrument. Integration of rotational rates, from gyro data for example, would result in drift. Therefore, although gyros are accurate in the short-term, accuracy in the long term is unlikely. Using absolute instruments such as compasses and inclinometers can result in an accurate measurement of attitude in the long term. However, in the short term, the physical nature of compasses and inclinometers, and the dynamic nature of a mobile platform result in highly volatile and therefore useless data. The solution then is to use both absolute and relative data. Kalman Filtering is known to be able to combine gyro and compass/inclinometer data to produce stable and accurate attitude information. Since the model of motion is linear and the data comes in as discrete samples, a Discrete Kalman Filter was selected as the core of the new estimator. Therefore, 'Estimate' can be divided into two parts: the Discrete Kalman Filter and the code framework.

Xu, Ru-Gang↗
NASA NTRS2002

Spacecraft Angular State Estimation After Sensor Failure

This work describes two algorithms for computing the angular rate and attitude in case of a gyro failure in a spacecraft (SC) with a special mission profile. The source of the problem is presented, two algorithms are suggested, an observability study is carried out, and the efficiency of the algorithms is demonstrated.

Bauer, Frank↗
NASA NTRS2001

Towards operational ocean state estimation

An assimilation effort is undertaken with the goal of understanding seasonal-to-interannual changes of the tropical Pacific Ocean from the late '80s to the present, encompassing the WOCE and TOPEX/POSEIDON (T/P) periods.

method↗
NASA NTRS2001

Stable and verifiable state estimation methods and systems with spacecraft applications

The stability of a recursive estimator process (e.g., a Kalman filter is assured for long time periods by periodically resetting an error covariance P(t.sub.n) of the system to a predetermined reset value P.sub.r. The recursive process is thus repetitively forced to start from a selected covariance and continue for a time period that is short compared to the system's total operational time period. The time period in which the process must maintain its numerical stability is significantly reduced as is the demand on the system's numerical stability. The process stability for an extended operational time period T.sub.o is verified by performing the resetting step at the end of at least one reset time period T.sub.r whose duration is less than the operational time period T.sub.o and then confirming stability of the process over the reset time period T.sub.r. Because the recursive process starts from a selected covariance at the beginning of each reset time period T.sub.r, confirming stability of the process over at least one reset time period substantially confirms stability over the longer operational time period T.sub.o.

Li, Rongsheng↗
NASA NTRS2009

Extended Kalman Filter for MMS State Estimation

The Magnetospheric MultiScale Mission is a four spacecraft formation flying mission designed to study the Earth s magnetosphere. The spacecraft fly in highly elliptical orbits, forming a tetrahedron at apogee. Each spacecraft spins at 3 RPM and is equipped with a star scanner, slit sun sensor, and accelerometer. The purpose of this work is to develop an Extended Kalman Filter to simultaneously estimate the attitude, angular velocity, angular acceleration, and center of mass of each spacecraft.

Markley, F. Landis↗
NASA NTRSDate not supplied

Real-Time State Estimation of Structural Modes for an Aeroelastic Wind Tunnel Model

A method is presented for estimating displacements, velocities, and accelerations of structural modes in generalized coordinates from measured sensor data in real time. Specifically, strain data from conventional strain gauges and fiber optic strain sensors (FOSS) were combined with the strain modes (obtained from a finite element model) in a least-squares estimator to produce structural mode displacement estimates and uncertainties. Likewise, accelerometer data were combined with displacement mode shapes in a second least-squares estimator to produce structural mode acceleration estimates. A Kalman filter was then used to refine the displacement estimates and obtain velocity estimates. The method was applied to the half-span wind tunnel test article used in the NASA-Boeing collaboration called the Integrated Adaptive Wing Technology Maturation (IAWTM) project. The technique was found to be useful for real-time control and system identification applications.

Jared A. Grauer↗
NASA NTRS1997

On-board State Estimation for Planetary Aerobots

Oscillatory balloon systems with telerobotic capabilities are being studied to support future space exploration by probes which will move up and down in a planetary atmosphere, land and explore numerous surface sites.

Planetary↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter utilizing Kalman-Schmidt and Rach-Tung-Striebel smoothers is developed to obtain entry vehicle atmosphere estimates. The filter/smoother blends information from pressure sensors distributed on the heatshield with measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars Science Laboratory and Mars 2020 entry, descent, and landing at Gale crater and at Jezero crater, respectively. The results show that the hybrid filter produces estimates of the freestream flight condition with lower uncertainty than either the flush or synthetic air data algorithms. The filter accomplishes this result by incorporating additional data and computing estimates of systematic error parameters in the pressure data and the aerodynamic model to further reduce the uncertainties.

Christopher D. Karlgaard↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter for entry vehicle atmosphere estimation is developed. The approach makes use of a Kalman-Schmidt and Rauch-Tung-Streibel smoother. The filter/smoother blends information from pressure sensors distributed on the heatshield with pseudo-measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars 2020 entry, descent, and landing at Jezero crater on February 18th, 2021. The final paper will also include results from the Mars Science Laboratory entry, descent, and landing.

Chris D. Karlgaard↗